Learning controllers for nonlinear systems from data

被引：6
|
作者：
De Persis, C. [1 ]
Tesi, P. [2 ]
机构：
[1] Univ Groningen, Engn & Technol Inst Groningen, NL-9747 AG Groningen, Netherlands
[2] Univ Florence, Dept Informat Engn, I-50139 Florence, Italy
来源：
ANNUAL REVIEWS IN CONTROL | 2023年 / 56卷
关键词：
Control design; Nonlinear control; Data-driven control; DATA-DRIVEN CONTROL; STABILIZATION;
D O I：
10.1016/j.arcontrol.2023.100915
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This article provides an overview of a new approach to designing controllers for nonlinear systems using data-driven control. Data-driven control is an important area of research in control theory, and this novel method offers several benefits. It can recreate from a data-centred perspective many of the results available in the model-based case, including local stabilization based on Taylor or polynomial expansion, absolute stabilization, as well as approximate and exact feedback linearization. Moreover, the method is analytically and computationally simple, and permits to infer regions of attraction and invariant sets, also when the data are corrupted by noise.
引用
收藏
页数：15
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